def test_set_model_time_limit(self):
model_type = "Xgboost"
automl = AutoML(results_path=self.automl_dir,
model_time_limit=10,
algorithms=[model_type])
automl._estimate_training_times()
print(automl._time_limit)
for _ in range(12):
automl.log_train_time(model_type, 10)
# should be always true
self.assertTrue(automl._enough_time_to_train(model_type))
def test_set_total_time_limit(self):
model_type = "Xgboost"
automl = AutoML(results_path=self.automl_dir,
total_time_limit=100,
algorithms=[model_type])
automl._estimate_training_times()
time_spend = 0
for _ in range(12):
automl.log_train_time(model_type, 10)
if automl._enough_time_to_train(model_type):
time_spend += 10
self.assertTrue(time_spend < 100)
def test_bin_class_01(self):
X = np.random.rand(self.rows, 3)
X = pd.DataFrame(X, columns=[f"f{i}" for i in range(3)])
y = np.random.randint(0, 2, self.rows)
automl = AutoML(
results_path=self.automl_dir,
total_time_limit=1,
tuning_mode="Insane",
algorithms=["Xgboost"],
)
automl._estimate_training_times()
self.assertEqual(automl._start_random_models, 15)
def test_set_model_time_limit_omit_total_time(self):
model_type = "Xgboost"
automl = AutoML(
results_path=self.automl_dir,
model_time_limit=10,
total_time_limit=10, # this parameter setting should be omitted
algorithms=[model_type],
)
automl._estimate_training_times()
for _ in range(12):
automl.log_train_time(model_type, 10)
# should be always true
self.assertTrue(automl._enough_time_to_train(model_type))
def test_enough_time_to_train(self):
model_type = "Xgboost"
model_type_2 = "LightGBM"
automl = AutoML(
results_path=self.automl_dir,
total_time_limit=10, # this parameter setting should be omitted
algorithms=[model_type, model_type_2],
)
automl._estimate_training_times()
for i in range(5):
# should be always true
self.assertTrue(automl._enough_time_to_train(model_type))
automl.log_train_time(model_type, 1)
